Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses

• URL: http://arxiv.org/abs/2402.17097v2
• Date: Fri, 12 Apr 2024 11:37:44 GMT
• Title: Re-Ex: Revising after Explanation Reduces the Factual Errors in LLM Responses
• Authors: Juyeon Kim, Jeongeun Lee, Yoonho Chang, Chanyeol Choi, Junseong Kim, Jy-yong Sohn,
• Abstract summary: We propose Re-Ex, a method for post-editing large language models (LLMs)-generated responses. Re-Ex introduces a novel reasoning step dubbed as the factual error explanation step. In addition to the explanation step, Re-Ex also incorporates new prompting techniques to reduce the token count and inference time required for the response revision process.
• Score: 9.956253757863145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mitigating hallucination issues is a key challenge that must be overcome to reliably deploy large language models (LLMs) in real-world scenarios. Recently, various methods have been proposed to detect and revise factual errors in LLM-generated texts, in order to reduce hallucination. In this paper, we propose Re-Ex, a method for post-editing LLM-generated responses. Re-Ex introduces a novel reasoning step dubbed as the factual error explanation step. Re-Ex revises the initial response of LLMs using 3-steps : first, external tools are used to retrieve the evidences of the factual errors in the initial LLM response; next, LLM is instructed to explain the problematic parts of the response based on the gathered evidence; finally, LLM revises the initial response using the explanations provided in the previous step. In addition to the explanation step, Re-Ex also incorporates new prompting techniques to reduce the token count and inference time required for the response revision process. Compared with existing methods including FacTool, CoVE, and RARR, Re-Ex provides better detection and revision performance with less inference time and fewer tokens in multiple benchmarks.

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